Data Science for Developers, How to become a data analyst?

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Data Science for Developers

Data science for developers is important, or should we say inevitable to learn as future trends suggest. With the rapidly advancing world, each discipline and field requires data experts to do data analysis, find solutions, and make their data more meaningful.

We can say that data science and data analysis is the backbone for the business of today to make important business decisions. In a previous article we discussed about Artificial intelligence for developers and how to learn artificial intelligence In this article, let’s find out what is data analysis and how important is data science for developers.

1. Data Science for Developers

2. What Is the Role of Data Analysts?

3. What is Big Data?

4. Structured data

5. Unstructured data

6. Semi-structured data

7. Why Should Developers Learn Data Science?

8. How to Become a Data Science Analyst?

9. Resources for Developers to Learn Data Science

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What is Data Science and Data Analysis?

Data science is an interdisciplinary field that focuses on tackling a broad set of structured and raw data and find actionable insights from it. Data science experts clean, prepare and analyze data using different techniques.

Data scientists do this to unearth answers incorporating machine learning, computer science, statistics, predictive analysis, and go through huge datasets to extract critical information and establish solutions.

The main goal in data science is to ask questions, understand data, and locate potential study avenues from a business point of view and give accurate predictions. The insights and predictions are then used to leverage important business decisions making data science for developers unavoidable.

On the other hand, data analysis involves processing and performing the statistical analysis of the existing datasets. Data analysts focus on creating techniques to obtain, process, and then organize the data to discover the solutions for current problems and methods to present data in the best way.

The results we get from data analysis can lead to instant improvements. Data analytics covers different branches of broader analysis and statistics that help to combine a diverse data source. When simplifying the results, it also helps to locate connections.

What Is the Role of Data Analysts?

The job of data analysts varies across companies and industries, but their responsibility is to solve problems and draw meaningful insights by utilizing data. They use different tool resources to analyze definite datasets and answer distinct business needs.

Such as how internal attrition in the company affects revenue, why in certain regions the marketing campaign went well, why sales index drop in a particular quarter, etc. All this makes data science for developers’ mandatory for their personal and corporate growth.

data science for developers

What is Big Data?

The term ‘big data’ describes the larger data volumes that inundate the companies daily. All of that data is not important. It depends on organizations that what they do with the critical data. The analysis of big data can lead to strategic business moves.

Big data is characterized in terms of;

  • Volume
  • Variability
  • Velocity
  • Variety

The importance of big data depends on how to deal with massive data.

The big data grows exponentially with time. For example, during 30 minutes of flight time, the jet engine can produce more than ten terabytes of data. With thousands of flights per day and with many hours of flight, the data generation touches up to many petabytes.

Big data is categorized into three forms;

  • Structured
  • Unstructured
  • Semi-Structured

Structured data

The structured data is the one that is stored, accessed, and processed in a fixed format form. With greater processing techniques, still, there are foreseeing issues with such massive data.

Unstructured data

Any data in an unknown structure or form is labeled as unstructured data. Apart from the vast data size, the unstructured data also brings multiple challenges in terms of deriving valued data while processing. An example of unstructured data is a heterogeneous data source with a combination of file formats.

Semi-structured data

Semi-structured data combines structured and unstructured data forms. Semi-structure data can be seen in structured form but not defined. The XML file is an example of semi-structured data.

Many big companies are turning towards big data for solutions. Big data analysis determines the root cause of defects, issues, and failures in near-real-time. Analyzing the buying habits of customers, a company can generate coupons from a sales point of view.

Big data analysis can recalculate all risk portfolios in a few minutes. It also helps to detect spams before it affects the company or organization.

data science for developers - why data science

Why Should Developers Learn Data Science?

Why is it important to learn data science for developers? Well, with passing time, the popularity of data science is growing and created many job opportunities. Data science engineers work on the data strategy on the front line. Data scientists are the first to handle the structured and unstructured data influx that enters the system.

For developers, data science has opened many new paths for them. They can get the opportunity to work with big and famous companies like Amazon, Google, Apple, and Uber as a data scientist. Many organizations sell their product and services by utilizing data science techniques.

Data scientists appreciate and value freedom when it comes to working. Being a data scientist does not mean that you are bound to work for any specific business. A key benefit is that you get the chance to the hands-on experience with that latest technology.

Unlike other IT jobs, the data scientists do not have to enroll in extensive training courses. Although extensive knowledge boost knowledge but basic training and certification of data science proves to be sufficient for most tasks. Rest, you will learn with time while working in this field.

As technology comes and goes, but when discussing data science, it’s the opposite case. This does not mean that data scientists or engineers stop learning more; with time, it will also become mechanized.

Solving real-world problems require people who can fit into various roles. Data science is a very exciting and extensive field. Every single second, small and big companies generate data daily.

Data scientists use a statistical approach to analyze data. Not every company makes full use of generated data. And this is where data scientists help businesses to run efficiently by playing their role.

Careers for data scientists are many. They can pick any path within this course as per their interest, such as;

  • Security
  • Project Management
  • Architecture
  • Consultancy, etc.

In these areas, data science experts are valued. Studies say that in the next years, the demand for data scientists will increase incredibly. So, we can conclude that there are many benefits of learning data science for developers.

How to Become a Data Science Analyst?

If you are interested in pursuing your career in data science, then you require to learn some related skills.

  • First, they need proficiency in one programing language and would be better if they have the working experience of a few more.
  • For data gathering, cleaning, statistical analysis, and visualization, usually R and SAS languages are used by data analysts. The key attributes of a good data analyst are creativity and curiosity. They need to parse through the problem with an analytic and creative lens.
  • For effective visualization of data, trial, and error techniques are used.
  • An expert data scientist knows to use which charts as per the audience, how to scale the visualizations, and type of graphs to use.
  • Some data analysts have a job to work on the back-end. They create a data warehouse by connecting databases from different sources. They find and manage data using querying languages.
  • Although machine learning is not an expected skill but data scientists who possess machine learning skills are greatly valued. SQL database is another learning requirement, which is a querying language with relational databases in a structured form.

Data is stored in tables, and the job of a data analyst is to extract the information from various tables and perform analysis. There are many variations of SQL language, such as PostgreSQL, PL/SQL (Procedural Language/SQL), T-SQL, etc.

  • Data analysts use different tools to collect unstructured data. Once enough data is gathered, they clean, mung, and process it through programming. Data analysts require advanced skills of Microsoft excel and must know the latest modeling and analytic techniques.

Whether it’s a team of executives making critical business decisions or readers, data analysts must convey their findings clearly to their audience effectively with strong communication skills.

Resources for Developers to Learn Data Science

As discussed earlier, data science is a vast field. Data cleansing, statistics, system design, programming, and anything else that is related to data depends upon the company size.

All these formats require resources to learn. Let’s start with the statistical part. Probably many of you were taken the basic statistics course in college.

You might find it tempting to start learning advanced statistical models and concepts. Most models and algorithms require some kind of hypothesis testing and accuracy.

  • Khan Academy and Duke University on Course are among the reputable resources to learn data science-related statistics.
  • Python is an interesting programing language and has many plausible sub-sections. Data scientists usually benefit from the operational and analytical aspects of Python. There are many books, video courses on the basics of Python for data scientists.
  • Once comfortable with Python, it will become quite easier to learn Pandas. It is a data manipulation language that enables basic analytics and transformations on datasets. There are great video lectures on YouTube to learn Panda.

Pandas do basic analytic functions. To easily apply advanced models, learn machine learning, and for that, go for ‘Simplilearn-Machine Learning with Python. It has newer content and goes much more depth for conceptual understanding.

  • Chris Albon’s book ‘Machine Learning with Python’ is another useful resource for learning. It starts with basic topics and then goes in more deep concepts such as neural networks. There are a few courses that will be helpful in machine learning.
  • Udemy offers courses on machine learning with Python. ‘Machine learning A-Z: Hands-on Python & R in Data Science’ is another comprehensive course about Python and R.

Python and R, not the only languages to learn data science, but they are mostly used. You can join Coursera to get certifications. So take time and see which way you will learn best either it’s from video lectures or books. There are many more sources to help you with your vision as data science for developers.


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